Scalability of Learners’ Success Rates in
e-Learning: A Survey Study of the Learners’ Perspectives

Abstract

Globally, ODL institutions experience
mismatch between scalability of numbers and scalability of success rates. This
study explored the scalability of success rates in open, distance e-learning as
perceived by the learners within the Chain of Response Model. The
primary aim of the study was to look at online learners’ success rate by
focusing on two institutional factors drawn from the Model, namely: the
learner’s study modules related challenges and support services. The results of
an online survey of 180 undergraduate and postgraduate online learners of
Egerton University, Kenya, showed: (a) the response rate of 16%; (b) a mixture
of hardware, software and personal factors were identified as pre-requisites
for e-learning success: (c) a number of mathematically-based modules were
identified as risks to success in online studies; and (d) while the learners
saw the learner support services as important they were less satisfied with
their provision. The present study points to two broad areas that require
further studies. First, qualitative look into specific challenges that learners
face with respect to learner support service provisions, modules interactivity,
and those identified as difficult to follow and thus posing risks to the
learners’ success. Second, investigation into tutor-learner contacts with the
view of identifying whether such contacts are reactive or proactive.

Introduction

Globally, throughput rate which is a
direct measure of a quantitative performance outcome of an institution is a
major challenge for open and distance learning institutions (Parker, 1995;
Perraton, 2007; Simpson, 2013). Simpson (2013) describes to this situation as
“distance education deficit”. Open and distance learning institutions have been
established to address the shortfalls of the conventional institutions with
respect to the democratisation of education-the provision of “education to as
many people as possible regardless of their inherent differences” (Latif,
Sungsri, & Bahroom, 2006; p.2). According to Henderikx (1999; p.30), the
British Open University was established with the aim of “democratization of
higher education giving a second chance to adult students who did not get an
academic degree for various reasons”. There are numerous endogenous and
exogenous factors that influence the students’ success rates in open and
distance learning. Some prior research indicates that while the open universities
have significantly scaled up access to higher education, the completion rate is
relatively low in comparison with traditional universities (Daniel, 1997;
Tresman, 2002; Perraton, 2007; Simpson, 2013). It is therefore evident that
while the scalability of numbers has been achieved, the scalability of success
rates is yet to be achieved and hence the mismatch is a worry from an
investment and reputation perspective. According to Alexander (2001; p.240)
e-learning has the potential to: “improve the quality of learning; improve
access to education and training; reduce the cost of education, and improve the
cost-effectiveness of education”. In this context, one element of
cost-effectiveness of education is the achievement of high success rate.

Cross (1981) developed Chain of
Response Model as a theoretical framework for explaining the learners’
barriers to participation in tertiary education. This framework classifies
learners’ barriers as situational, institutional and dispositional. Garland
(1993) expanded on the Chain of Response Model proposed by Cross (1981)
from a three-barrier structure to a four-barrier structure by including one
additional barrier, the epistemological barrier. In summary, in this context,
Garland’s (1993) categorization of barriers that scale down learners’ success
rate are: situational, dispositional, institutional and epistemological. Within
the Cross’s and Garland’s classification framework, these factors include inter
alia: technological enhancement, learning support services, demographic
profile, institutional framework, pressure group and success measures. Given
the interactive nature of these factors it is difficult to determine a single
causal explanation for scaling up success or decreasing attrition rate in ODL
(Latif, Sungsri, & Bahroom, 2006). Despite this challenge the mandate of
ODL institutions remains that of massification of education and hence the need
to pay special attention to scalability of success rate of learners through
identification of interventions that can be put in place. The implications of
scaling success rates include: timely enhancement and production of human
capacity for national wealth creation; enhancing the reputation of ODL
institutions; reducing the cost of education and training; increasing the
revenue base of the institution as more students are attracted by the success
rate; and increasing the learners’ market opportunities for promotion and
mobility (World Bank, 2000a; 2000b; Alexander, 2001; Olakulehin, 2008).

Technological Enhancement

Throughout its development ODL has made
use of various technologies to enhance the learners’ success in their studies.
At one point in time print technology was the only mode of delivery of ODL
(Henderikx, 1999; Ortner, 1999; Heydenrych & Prinsloo, 2010) and with advances
in technology, combination of technologies can now support learning in any
location leading to a situation that has been referred to as a learning-anytime, anyplace and anywhere learning (Khan, 2000). The challenges
of loneliness and isolation of ODL learners is now being addressed by
e-learning platforms such as Learning Management System (LMS) or Sakai and
mobile phone devices. These technologies are enhancing tutor-learner and
learner-learner interactions and consequently the success rate of the learners.
These experiences remove isolation challenges. In this regard, e-learning has
been seen as a panacea for bridging transactional distance. However, the use of
technology and in particular e-learning to support learning in developing
nations has encountered barriers that have limited its extensive use and hence
the learners’ success (World Bank, 2003; Abdon, Ninomiya, & Raab, 2007;
Gunga & Ricketts, 2007; Wright, Dhanarajan, & Reju, 2009). These
barriers include: power and internet connectivity, telecommunication
penetration, cost of hardware and internet access, and human capital for
development of online courses. Invariably, evidence also shows that even in
developed nations, technology has not in all situations scaled up success rate
among the learners. The evidence is a mixture of successes and failures. As
observed by Simpson (2003) online learning is not a panacea of scaling up
success rate in ODL. A study by Levy (2007; p.185) reported that “students
attending e-learning courses dropout at substantially higher rates than their
counterparts in on-campus courses”. Carr (2000) and Flood (2002) reported that
online learning register up to 80% and 70% dropout rate, respectively.

Where high success rate in ODL has been
reported, it has been attributed among many other factors to: age of the
learner; learner’s experience and receptivity to computer use; and the
learner’s seniority in employment. In these contexts, it has been shown that
younger learners perform poorer in ODL mode of study than adult learners; that
prior experience with computers and internet and receptivity are positively
related to acceptance of an online course; and that those in senior management
positions do not want to be seen by the employees as either failures or
dropouts (Harris & Gibson, 2006).

Unlike the developed nations, the
developing nations are in a unique situation with regards to the use of
technology in teaching and learning across all levels of education. While the
telecommunication infrastructure has made mobile phone utility possible in many
remote parts of Africa, its use in scaling up success rate of ODL learners is
yet to be tested. Recent findings indicate that learners’ support by mobile
phone is possible primarily on administrative and consultation matters (Hendrix,
2008; Maritim & Mushi, 2011). However, the small storage capacity and
screen of the mobile phones that are affordable make them ineffective for
serious academic work. The larger screen smartphones are as good as laptops but
they are expensive for low income distance learners.

Learning Support Services

One of the key pillars of success in
distance learning is the learner support services (Hardman & Dunlap, 2003).
According to Thorpe (2001) learner support services are those parts of a
distance or electronic learning system, which are additional to the provision
of course content. Learner support has been placed in three non-exclusive
categories, namely: academic support, personal support, and administrative
support (Tait, 2000; Thorpe, 2001; Simpson, 2002). These services are important
in assisting the learners to overcome barriers to learning, reduce the
isolation, facilitate effective learning, reduce attrition rates and scale up
their success rates (Nonyongo & Ngengebule, 1998). Simpson (2002) observed
that unless the learning support services are appropriate and valued by the
learners they will have no impact on improving the learners’ success rate.
Invariably, Rowley (1996) noted that “successful study can rarely be achieved
if other areas of a student’s life are unbalanced or causing problems”. This
observation is supported by Latif, Sungsri, and Bahroom (2006) and Carroll, Ng,
and Birch (2009) findings that factors that cause attrition in ODL are mainly
situational and dispositional in nature. The support services come in different
forms including: counselling and guidance, provision of learning materials such
as course outline/modules, provision of tutorial letter/tutorial classes,
feedback on assignments, provision of limited face-to-face sessions, consultations
online and off-line, computer services, library services, group discussions,
family support, peer-group support, time-off in case of employed learners and
administrative (Nonyongo & Ngengebule, 1998; Chua & Lam, 2007;
Oosthuizen, Loedolff, & Hamman, 2010). Where these support services are low
such as in situations where the learners experience delays in the delivery of
study materials and guidelines or lack of proactive contacts with tutors and
supervisors, these situations are more likely than the teaching method per
se to scale down the success rates (Kaye & Rumble, 1991; Carroll, Ng,
& Birch, 2009; Bhalalusesa, 2009; Chabaya, Chiome, & Chabaya, 2009;
Kamau, 2010; Risenga, 2010). Similarly, studies have shown that only quality
services that are appropriate and valued by learners have the potential of
scaling up the learners’ throughput rates (Simpson, 2002). Indeed, if an ODL
institution fails to address the quality of learning support services, it runs
the risk of delivering poor education (Chua & Lam, 2007) and scaling down
the learners’ success rate (Hardman & Dunlap, 2003).

Measurement Challenges

The measurement of success rates of ODL
learners is a contested issue. In a traditional education system completion
rate of a cohort of learners is a measure of the success rate and conversely,
it is a measure of internal efficiency of an education system or institutional
performance (Woodhall, 1985). In this context, high non-completion rate of
learners gives a bad reputation to an institution as it is associated with
institutional inefficiency and poor performance (Ashby, 2004; Latif, Sungsri,
& Bahroon, 2006; Tyler-Smith, 2006). Though teacher education programmes
conducted through ODL register high success rate of up to 90% (Perraton, 2007),
it is estimated that on the average non-completion rate in ODL institutions is
higher than in traditional institutions by between 10% to 20% points (Carr,
2000; Diaz, 2002; Simpson, 2004; 2013; Levy, 2007; Perraton, 2007). It is this
low success rate and the long period students take to complete their programmes
that have given the impression that ODL institutions are not cost effective
when compared with the conventional institutions (Tyler-Smith, 2006; Perraton,
2007). The measure of completion, retention and transition rates in ODL system
is confounded by some of the attributes that make it a preferred mode of study.
Though flexibility is a highly valued attribute of ODL, it brings in the
problem of the measurement of the completion rates. Flexibility masks the
provision of clear definition of the learner’s year of study and fulltime
equivalent student. The concept of “active students”, a term popularly used in
ODL institutions to refer to the proportion of students who take at least one
module in a semester, can neither be used to measure dropout rate nor success
rate. A student who may be active in one year may step out of the programme for
two years before resuming his/her studies. This intermittent study approach
renders measures of ODL success rate rather complicated. The traditional
computation of successful completion rates takes into account completion period
by members of a cohort who began studying in the same year. In view of ODL
flexibility members of a cohort who all began “studying in the same year may
graduate between three and ten years later” (Perraton, 2007; p.95). Computation
of completion rate is also confounded by under reporting of failure and dropout
rates in ODL institutions (Calvert, 2006).

Institutional Framework

There are two primary variants of ODL
delivery institutions, namely: the single and dual mode (bimodal) institutions.
The All-Africa Ministers’ Conference on Open and Distance Education (2004)
differentiated the two types of institutions as follows: dual and single mode
institutions (SAIDE, 2004). The dual mode institution offers learning
opportunities in two modes: one using traditional classroom-based methods, the
other using distance methods; the same courses may be offered in both modes,
with common examinations, but the two types of learners-on-campus and
external-are regarded as distinct. The single-mode institution is an
institution that has been set up solely to offer programmes of study, either at
a distance, or face-to-face. There has been a debate of whether the type of the
ODL institution has differential impact on the students’ service delivery. The
proponents of dual mode system argue that off-campus students have the advantage
of being taught and examined at the same level as conventional students by use
of similar instruments by the same tutors. On the contrary, studies show that
off-campus learners are given less attention by conventional tutors and that
tutors see their participation in off-campus teaching and consultation with
learners as part-time activity (Siaciwena, 1983; Maritim, 2009). Similarly the
financing and capacity building of the distance component in dual mode
institution is poor as the unit is given peripheral status in the university
(Kamau, 1999). However, the there are few reported exceptions. Deakin
University in Australia is a success story of a dual mode institution (Davies
& Stacey, 1998). Investigation in to the throughput rate of ODL component
in dual mode has not received attention. This is masked by lack of segregation
of graduation figures into on-campus and off-campus streams.

Pressure Group

ODL students exercise limited pressure on
the management of the institution and hence can be exploited in terms of
resource allocation and service provision (Glennie, 2008). Unlike their
conventional counterparts, the union of the ODL learners, where available, is
structurally weak to tackle administrative weaknesses of the education
provider. In view of their spread, physical isolation and their virtual nature
they rarely have the structure to organize a strike as a way of registering
their grievances or putting pressure on the institutional management for better
provision of learner support services.

Statement of the Problem

Despite the national and the
institutional challenges identified by Gunga and Ricketts (2007) and Kenya
Education Network (2013) a good number of conventional universities in Kenya
are now embracing e-learning mode of delivery of their programmes. Globally, online
learning is a growing phenomenon that the developing countries cannot afford to
ignore how it is transforming higher education landscape. As Betts (2017) puts
it, online learning is a “new norm” in higher education. In USA, approximately
a quarter of higher education students are taking at least one online course
(Smith, 2016). In the Kenyan situation, the mainstreaming of e-learning in
higher education is driven by a number of factors including inter alia;
government policy to expand ODL in existing universities by levering on ICT,
the technological advances, inefficiency and high cost of print-based delivery
system, and the need for the University to offer courses across the border;
e-learning being seen as a potential source of revenue generation; and the
anticipated cost effectiveness if the numbers can be scaled up. However, the
major challenges these dual mode institutions face include inter alia:
their low level of e-readiness (Kenya Education Network, 2013) and their
component of learner support services suffer like in other developing countries
from tutors’ duality of assignments through engagement in both conventional and
e-learning streams (Kamau, 1999; Maritim, 2009).

While many higher education institutions
in Kenya, including Egerton University, are moving towards being dual mode
institutions and want to embrace technology-mediated learning, one question
that arises is the degree to which these institutions are balancing their
ambition of scaling up the student numbers in order to raise higher revenue
with the learners’ satisfaction of the support services. However, one needs to
see institutional desire for scalability of numbers through e-learning in
relation to learner support services. While looking at quality assurance on
online learning, Chua and Lam (2007; p.151) observed that “one of the purported
benefits of online education is the ability to scale beyond the limitations
inherent in a brick-and-mortar institution…. and there is a need to conduct
research into how online educational modes can be made to scale successfully
without compromising the quality of the educational experience”. Hardman and
Dunlap (2003) add that the major challenge to online education providers is not
so much how to recruit students but how to retain them once they have begun.

Purpose of Study

During 2014/2015 academic year, Egerton
University ventured into e-learning mode of delivery.

This study focuses on online learning
support services as experienced by the learners. It interrogates scalability of
success rates from the learners’ perspective. The main objective of the study
is therefore to contribute to identification of support elements that scale up
the success rate in a dual mode institution as perceived by the learners. The
specific objective is to examine the learners’ perception of the importance and
satisfaction with support elements and the study modules provided for their
online learning. This objective is based on the assumption that when students
are satisfied with their online courses, their success and retention rate in
the course is high (Palloff & Pratt, 2007).

Methods

Subjects

The subjects that constituted the study
population were all 180 active learners who were enrolled in online
undergraduate and postgraduate programmes of Egerton University, Kenya, during
the first semester of 2015/2016 academic year. Twenty-three were men and five
(18%) were women. Their ages ranged from 21 to over 40 years, with 61% above 31
years old.

Materials

The whole instruments had both open and
structured questions that covered: the demographic questions; preference for
ODeL questions; modules perceived as high risk, and questions focusing on the
perceived level of importance and satisfaction with support services. The
demographic questions included those that have been used in previous ODL
research. There are age and gender (Simpson, 2006; Sharma & Samdup, 2009; Gonzalez-Gomez, Guardiola, Rodriguez, & Alonzo, 2011). On a 5-point Likert
scale, adopted from Rekkedal and Eriksen (2004) instrument, the respondents
rated each of the 13 support elements on the degree of their perceived
importance and satisfaction in enhancing their online learning success.

Procedure

All participants completed an online
questionnaire on a voluntary basis. They were informed about the purpose and
procedure employed in the study and the responses were de-identified. The
descriptive analyses and correlations of research variables were generated. The
google form in which the data was collected has an in-build statistical package
that generates various descriptive statistics. The Pearson correlation
coefficient was computed through excel programme.

Results

The descriptive analyses and the
correlations show the following scenarios:

Demographic Profile

There was a male dominance among the
respondents, 82% and 18%, males and females, respectively, and most of the
respondents, 61%, were over the age of 31 years. A recent survey shows that
approximately 56 million students who study through online mode female make up
56% (OnlineUniversities.com).

Response Rate

The response rate received from the
sample was 16%. This rate is lower than what has been reported in the
literature of between 20% and 40%, an average of 30%, for online surveys. This
low response rate may be attributed to non-repeat reminder e-mails to
non-respondents and small sample size (Nully, 2008).

Online Preference

The reasons given by respondents for
preferring to pursue online learning varied and included: work
commitment/engagement; convenience; flexibility; light duty in workplace;
distance from town/university; further education; and cost. Similar factors
have been reported in other studies (Hardman & Dunlap, 2003). The reasons
given give a clear indication to e‑learning providers that online
learners are looking for convenience and flexibility in their studies.

Online Learning Pre-requisites

Pre-requisites are measures of the
learner’s readiness for online learning. As an assisted-technology learning,
e-learning puts some demands on the learner. The success behind e‑learning
is anchored on the ability of the learner to navigate through a learning
platform, the Learning Management System. This therefore calls upon the learner
to acquire certain pre-requisites or being in an environment that is
technologically enhanced. This survey indicated that for an online learner to
succeed he/she needs to have: computer literacy; possession of laptop/computer;
availability of internet/connectivity; time management; self-discipline; power
connectivity; Internet reliability; discussion groups; quick response from e‑learning
tutors; and administration’s response towards students’ grievances.

Difficult Modules

Though modules are developed with the
learners’ profile being taken into consideration and are pretested before
release for use by learners, e-learning is still at infancy stage at Egerton
University and the module developers have not acquired adequate experience. Any
difficulty a learner encounters poses a potential risk of failure to complete
the course. The modules that the learners found difficult to follow and hence
pose potential for failure are: Business Mathematics 1; Introduction to
Computers and Computer Applications; Philosophy and Society; Principles of
Micro-economics; Academic Communication Skills; Management Mathematics and
Principles of Accounting II. The reasons for these courses being
considered risky by the learners were not investigated.

Online Risks

Provision of online learning has its
unique risks. These include poor financing of online unit and tutors’ duality
of roles (Kamau; 1999; Maritim, 2009). These risks affect the quality of
learner support service provision. In this study, respondents identified the
following as prevalent risks in their studies: time for library; working on
practical; support and resources needed; networking; missing information when
offline; too much assignments; lack of student-institution communication; lack
of Internet/Wifi connectivity; late registration hence late receipt of modules;
speed of response to students issues/academic queries from tutors; pressure of
work leading to: limited study time and limited time off for seminars; failure
to access tutors and unfavourable mode of assessment.

Some of these risks have been reported in
other studies (Olibie, Offor, & Onyebuchi, 2016). The major risks echoed by
the learners point to five things: First, to what Gunga and Ricketts (2007)
identified as the drawbacks in e-learning in African universities, namely:
power and internet connectivity. This affect learners in locations which are
technologically disadvantaged. Second, concern raises the need for
e-assessment. Lack of e-assessment is a major risk and barrier to expansion of
e-learning across the borders. Currently all online students are required to
present themselves physically at the University campuses for final assessment.
The third risk arises from support service provision where tutors have low
interactions with their students. This is a prevalent risk in dual mode
institutions where tutors experience duality of roles and hence take online
role as a part-time assignment. Fourth, the need for virtual laboratory for the
demonstration of practically-related learning is an added value to online
delivery. Fifth, the low level of networking among the learners seems to exist.
This suggests that learners are not having an opportunity to support each
other. Learners’ networking in ODL is a key success element and hence warrants
attention.

Support Elements

Support elements enhance the learners’
satisfaction with the course, a factor that is crucial to online learners’
retention (Levy, 2004). The means and standard deviations for learners’
importance and satisfaction ratings for each support element are presented in Table 1. As per the Likert scale administered, the lower the mean the
higher the importance and satisfaction of the support elements. For each
support element, the learners’ means rating is higher on the perception of the
support elements as more important than on their satisfaction with their
provision.

Table 1: Means and standard deviations
for learners’ ratings of importance and satisfaction with support elements

The mean scores expressed in Table 1 show the support element of discussion, online chats and accessibility to
tutors as being low on satisfaction dimension. These are the core pillars on
online success and yet the tutors appear least concerned. These support
elements provide social contacts that are the recipes for successful completion
of an online programme. Studies have shown that success and retention in online
learning is enhanced by human contacts (Palloff & Pratt, 2007). This
implies in the case of this study the need for the considerations of maximizing
in the course of learning both synchronous and asynchronous communications. The
utility of both the chats and discussion fora is provided in the LMS. If tutors
use LMS chats provision, they are able to satisfy the learners’ desire for
enhancing synchronous or tutor-learner real-time interactions. Further, as
value addition, human contacts can be enhanced by the providing or adding
visual and audio files to an asynchronous online study module. This makes a
virtual classroom to mirror a conventional classroom where a learner sees and
hears the teacher.

The correlation between mean importance
and mean satisfaction is depicted in Figure 1. This correlation of r = .32
is lower than the r of .83 found by Mitra (2009) in a study among the Open
Schooling learners in India. This finding (r = .32) suggests that
while the online learners saw the identified support elements as important for
their learning success they were less happy with their provision.

Conclusion and Further Research

While the utility of e-learning in higher
education is a new norm (Betts, 2017), its full benefits to learners is
yet to be realized. Higher education institutions in developing countries are
too futuristic when embracing e-learning. The environment where e-learning is
being offered has its uniqueness. This include: inexperience tutors involved in
the provision of learners support services; and the institutional and the learner’s
readiness for the new norm. The present study points to two broad areas
that require further studies. First, qualitative look into specific challenges
that learners face with respect to learner support service provisions, modules
interactivity, and those identified as difficult to follow and thus posing
risks to the learners’ success. Second, look into tutor-learner contacts with
the view of identifying whether the contacts are reactive or proactive and the
need to address low tutor-learner interactions.

References

Abdon, B. R., Ninomiya, S., & Raab, R. T.
(2007). E-learning in higher education makes its debut in Cambodia: The
Provisional Business Education Project. International Review of Research in
Open and Distance Learning, 8(1), 1-11.

Glennie, J. (2008). A Critical Overview of
Quality Assurance and Accreditation Policies, Structures and Practices for ODL
in Africa: Gaps, Challenges, and Lessons for the ACDE continental initiative. Paper presented at the African Council for Distance Education Stakeholders
Workshop held at the University of South Africa, Pretoria, February 21-23.

Kamau, J. W. (2010), Factors that affect the
progress and retention of distance learners in the Diploma in Primary Education
Programme in Botswana. Progressio: South African Journal for Open and
Distance Learning Practice, 32(2), 164-180.

Perraton, H. (2007). Open and distance
learning in the developing world. London: Routledge.

Pierrakeas, C., Xenos, M., Panagiotakopoulos, C.,
& Vergidis, D. (2004). A comparative study of dropout rates and causes for
two different distance education courses. International Review of Research
in Open and Distance Learning, 5(2).

Thorpe, M. (2001). Learner support: A new
model for online teaching and learning. Paper to the 20th ICDE
World Conference, Dusseldorf.

Tresman, S. (2002). Towards a strategy for improving
student retention in programs of open, distance education: A case study from
the Open University UK. International Review of Research in Open and
Distance Learning, 3(1), 2-11.

Tyler-Smith, K. (2006). Early attrition among
first time e-learners: A review of factors that contribute to drop-out,
withdrawal and non-completion rates of adult learners undertaking e-learning
programmes. MERLOT Journal of Online Learning and Teaching, 2(2). Retrieved
from http://jolt.merlot.org/Vol2_No2_TylerSmith.htm